Yamamura, K (2012) Extended key-factor/key-stage analysis for longitudinal data. Journal of Biopharmaceutical Statistics 22:1–15. [Preprint PDF(282KB)]

Abstract

Key-factor/key-stage analysis was originally a descriptive approach to analyze life tables. However, this method can be extended to analyze longitudinal data in pharmaceutical experiments. By dividing the variance into components, the extended key-factor/key-stage analysis indicates which factor is influential, and through which stage the factor generates its influence in determining the outcome of treatments. Such knowledge helps us in constructing a class of nonlinear longitudinal models that can be interpretable than linear models. Example SAS programs and R programs are provided for the calculation.

Please download the R funciton and SAS macro for performing key-factor/key-stage analysis